Hopelessly optimistic. Spoken like someone who has never taken an experimental design course imho.
1) The search space is practically infinite dimensional. All methods suck when trying to extract a causative model from an infinite search space. There's a reason why in experimental design we change as little as possible.
2) SNP's are not the entire story. If it were this simple we would have progressed much further already. See dismal failure of all other high throughput sequencing and microarray technology. We still don't know how to analyse this stuff properly, if it will ever be possible.
3) The metabolome is adaptive! While we each have different enzyme kinetics due to slight differences in protein makeup, overall metabolic flux rates are amazingly consistent. See Oliver Feihn et al for more details.
I meant on the web, personal stories/advice/what-not-to-dos of entrepreneurs in the startup realm. I wasn't suggesting picking up a text book. My background isn't business per say, but I've learned a great deal by reading and researching. I also took several business related classes in law school so maybe that makes it easier for me to grasp.
Sold instructions on ebay about how to beat a little known tax law [it's now plugged]. A friend went to work for the tax man and in training they were told about me and how it's so important to not let these tricks get out, as it could cost the country lots of money. I promptly stopped as I didn't realize I was on their radar!
Reverse engineered an algorithm from a huge international company who kept it under lock and key using publicly available data and used it with their competitors. They still have it under lock and key.
Bought textbooks at fundraising sales and sold them to university students the next year for a 3000% markup.
Aren't those little known tax laws usually easy for them to give you a ton of crap about, disregard and have you go through courts to actually get the benefit?
The smartest people I know have the following in common:
- They are the most knowledgeable person in the room in their topic of interest. That takes obsession.
- They surround themselves by other smart people constantly
- In large groups of smart people they are perfectly happy to ask questions even if they are wrong. E.g in a maths seminar will debate, and often get beaten by the subject expert.
I often wonder if the latter - asking "stupid" questions to experts in a field - is something that was there before they were at the top. Anyone else noticed this?
Even though code is text, you would think someone would have tried to build a different view given the popularity of MVC.
Inline expansion would be nice. So would code paths. So would inline images & html rendering for documentation. So would the relevant rendered html for the issue relating to the code you are looking at. So would other annotations. And what about visually hiding code when working in a language like java?
I tried to get this going in eclipse once, and it really wasn't going to happen easily so I gave up. I've always hoped someone would build something similar as I feel it would really help productivity having everything relevant right there with the code.
Note: The project http://code.google.com/p/lambda4jdt/ is the closest thing I have found to what I am suggesting which I think shows the power. It just goes to show that it could work.
Indeed, MS are trying to encourage this with the new WPF-based editor in VS2010. The developer's examples are all about graphical overlays replacing certain types of code. It will be interesting to see if ISVs take this forward.
Let's say you had an eclipse plugin that let you write in lisp, but it actually converted the code to java underneath. You could click a button to switch between views. Are you saying that would be a bad thing? If a tool allows you to be more productive, I use it. Note: I don't use lambda4jdt at all, I just like the idea of creating problem specific views for code and I don't think that belongs at the language level.
You're right, my comment was directed at your example really; the code before and after use the same representation, text. In that case it seems the original was simply a poor textual representation of the logic. I'm actually a firm believer in being able to visualise code. After all coding really gets done in my head and when it's in there it's in 3D images/videos. I long for the day when I can work in an immersive environment where I can walk around the design suspended in the air. That day seems a long way off at the moment though :)
Honestly, the world isn't black and white like that. And I can see why you argued about it; that would drive anyone mad being told what they do and don't like based on a histogram of time spent in the last 2 years.
There are many explanations for not spending time doing things you like; competing priorities is the main one (i.e. I like other things more than them) and also environment (perhaps you enjoy kayaking but hate the drive to get there, or don't want to go with johnny and max because you don't like max).
Just because you don't do something doesn't mean you don't like it. It's a false dichotomy.
I never said anything to the contrary. I said that it's a false dichotomy; just because you don't spend time doing something doesn't mean you don't like it, just that you may not like doing it as much as other activities.
I love playing games, but never do because I like other things (learning mainly) far better. I also love kayaking, but would much prefer to go to the gym because of convenience. That doesn't mean I don't like playing games or kayaking; I really enjoy doing both. Get it?
But, at the same time, claiming to like something doesn't (for sure) mean that you do like doing that something. We can be lying to our partners, to the world, to ourselves.
Especially bittersweet are the times you predict your partner's taste in something, they deny it, and a few months later it turns out you were right after all.
Once an itch is scratched it turns into a scab. And new itches appear.
Seriously though, businesses change over time. Are people who are great at getting things going the right people to run them long term? In some cases yes, in others no. In my case I plan to be involved in one of my companies for a long time, but for another I am involved in we are building to exit.
I completely agree. Companies change over time. The startup you founded with one or two other people eventually becomes a business.
Now there are typically two ways of running a successful business: (1) culture; or (2) process.
If you build a business with a very specific culture that you will love to work within as the company grows then great. However building a business is much easier than establishing a culture you want that grows with the business.
Process is the alternative to culture as a route to long-term success and profitability. When you can't hire smart people that will maintain a company culture, you have to rely on process to scale. Even Warren Buffett once quipped, "I try to buy stock in businesses that are so wonderful that an idiot can run them. Because sooner or later, one will."
Once you take you business down the path to process instead of culture, you are creating a monster that hopefully craps cash. Eventually that monster becomes so big and powerful that your position as a leader becomes a position as a process manager.
Leaders lead people. Managers manage processes.
Processes don't have room for vision. Leadership does.
Once you have processes, you start looking for an exit strategy.
Besides the issue of culture versus process, many entrepreneurs also enjoy the act of creation most. These entrepreneurs are like artists. Once they've created something, they desire to move on to the next piece once they are satisfied with their current piece. The different between the entrepreneur and the artist is that for the entrepreneur it may take months or years before they are satisfied with their current piece, whereas the artist may take days to weeks to months to finish a piece and move on to the next.
Plus, deciding to move on to the next piece doesn't mean that you cannot stay on as an advisor to those who will continue to work on your previous pieces.
Years ago (pre swivel, post quantrix/tableau) I made a competitor as well. We quickly decided once we went out and talked to potential customers that it was a no go.
1) Very few customers wanted to upload their data to the web. Even less were allowed by law.
2) Most had integration problems - i.e. they couldn't actually access their data because it was in disparate systems.
3) Most people didn't actually use the information for anything, it was simply to provide them with "evidence" that they were right before looking at the charts. As a statistician this makes me sad, but it was the way things were at least when we investigated it.
Over time we gradually realised why BI was priced so high; it needs system integration alongside it. Now it turns out that we may have been wrong (gooddata.com are doing a good job as far as I can tell) but I still think it's the tip of the iceberg in terms of potential market.
My advice: target a niche with a huge problem where the customer is unable to get at their data at the moment. Choose the niche which has 1-2 big vendors of data collection systems with 80+% combined market share and provide the solution to the problem. And charge.
Thanks a lot for detailed reply. I would appreciate it if we could discuss the theme a bit more. May I ask you about your contacts? My contacts are in my profile.
The odds are stacked against you if you don't know the area you are in. This is because you can't control the competency of the people you hire or the work they do, so you have a random factor that has a direct and long lasting influence on your companies success.
After my first software startup I tried a hardware product and failed miserably because of my lack of knowledge. To remedy this for next time I am formally learning the areas where we failed (electronics + cad) and when I'm ready I will try again.
Theres so much that can go wrong with a startup that it's just not worth adding another element into the mix.
Thanks for sharing your insights. How much do I really need to "know" the area? Do you mean actually knowing what's going in the technology or just an understanding of industry/process?
I assume the next time you see an opportunity in the hardware business and want to jump onto it, you wouldn't be taking a 4 year undergrad course to know the drill.
And what's your experience of "learning by doing"?
You need enough knowledge to know when someone is making the right decision or not for your business. I run a successful software company, have hired and managed many programmers and still got it wrong with hardware.
>I assume the next time you see an opportunity in the hardware business and want to jump onto it, you wouldn't be taking a 4 year undergrad course to know the drill.
That's actually exactly what I'm doing - although after you have a degree it only takes a year or so to go through the relevant programs, and if you know people in the university it's easy to get private tutoring for specific gaps. It's not like the courses are difficult once you learn how to learn. Plus, you get to see who would be a good fit in your next startup.
Learning by doing: Perhaps I'm just not smart enough, but I need to understand some theory before I can do it right in practice, and I learn best talking to knowledgeable people. I fumble and make too many costly mistakes when learning something completely new without guidance. We all have our weak points :)
1) The search space is practically infinite dimensional. All methods suck when trying to extract a causative model from an infinite search space. There's a reason why in experimental design we change as little as possible.
2) SNP's are not the entire story. If it were this simple we would have progressed much further already. See dismal failure of all other high throughput sequencing and microarray technology. We still don't know how to analyse this stuff properly, if it will ever be possible.
3) The metabolome is adaptive! While we each have different enzyme kinetics due to slight differences in protein makeup, overall metabolic flux rates are amazingly consistent. See Oliver Feihn et al for more details.